Economic Load Dispatch with Daily Load Patterns and Generator Constraints by Particle Swarm Optimization
This paper presents an optimization technique to economic load dispatch (ELD) problems with considering the daily load patterns and generator constraints using a particle swarm optimization (PSO). The objective is to minimize the fuel cost. The optimization problem is subject to system constraints consisting of power balance and generation output of each units. The application of a constriction factor into PSO is a useful strategy to ensure convergence of the particle swarm algorithm. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. The performance of the developed methodology is demonstrated by case studies in test system of fifteen-generation units. The results show that the proposed algorithm scan give the minimum total cost of generation while satisfying all the constraints and benefiting greatly from saving in power loss reduction
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059667Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1531
 Y. Ting-Fang; P. Chun-Hua, "Application of an improved Particle Swarm Optimization to economic load dispatch in power plant," in Proc IEEE Int. Conf. Advanced Computer Theory and Engineering, 2010.
 A. J. Wood, and B. F. Wollenberg, Power Generation, Operation, and Control, New York, John Wiley & Sons, 1996.
 K. P. Wang and C. C. Fung, "Simulate annealing base economic dispatch algorithm," IEE Proc C vol.140, no.6, pp. 509-515, November 1993.
 J. Y. Fan, and L. Zhang, "Real-time economic dispatch with line flow and emission constrains using quadratic programming," IEEE Trans. Power Systems, vol. 13, pp. 320-325, May 1998.
 D. C. Walters and G.B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. Power Systems, vol. 8, no. 3, pp. 1325-1332, August 1993.
 M. Lin, F. S. Cheng and M. T. Tsay, "An improved tabu search for economic dispatch with multiple minima," IEEE Trans. on Power Systems, vol. 17, no. 1, pp. 108-112, February 2002.
 J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
 A. Mahor, V. Prasad, S. Rangnekar, "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, vol. 13, no 8, pp. 2134-2141, October 2009.
 Z. L. Ging. "Particle swarm optimization to solving the economic dispatch considering the generation constraints," IEEE Trans. power system, vol. 18, no.3 pp. 1187-95, August 2003.
 J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Trans. on Power Systems, vol. 20, no. 1, pp.34-42, February 2005.
 Y. Fukuyama, "Fundamentals of particle swarm optimization technique," in Modern heuristic optimization techniques: theory and applications to power system, K. W. and M. A. El-sharkawi Eds. New Jersey: John Wiely & Sons, 2008, pp. 74-75.
 A. P. Engelbrecht, Computational Intelligence: An Introduction 2nd ed. West Sussex: John Wiley & Sons Ltd. 2007.
 H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama and Y. Nakanishi, "A particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Trans. Power Systems, vol. 15, pp. 1232-1239, November 2000.
 H. Saadat, “Power System Analysis,” McGraw-hill companies, Inc, 1999.